{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4050dc65",
   "metadata": {},
   "source": [
    "## 用神经网络逼近任意函数\n",
    "这个notebook展示了使用DeepXDE逼近任意函数。\n",
    "\n",
    "\n",
    "\n",
    "1. [$y = a_n x^n + a_{n-1} x^{n-1}+ \\dots + a_1 x + a_0$](#func1)\n",
    "2. [$y = x \\sin(ax+b)$](#func2)\n",
    "3. [$y = e^x$](#func3)\n",
    "4. [$y = \\ln x$](#func4)\n",
    "5. [$y = \\tan x$](#func5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ee60b29",
   "metadata": {},
   "source": [
    "### 基本初等函数\n",
    "\n",
    "数学分析将基本初等函数归为六类：幂函数、指数函数、对数函数、三角函数、反三角函数、常数函数。\n",
    "\n",
    "$$\n",
    "\\large\n",
    "\\begin{aligned}\n",
    "&1. y = c, &&c \\in \\mathbb{R}, && x \\in \\mathbb{R}, &&y = c,\\\\\n",
    "&2. y = x^{\\alpha}, &&\\alpha \\in \\mathbb{R},&& x \\in \\mathbb{R}, &&y \\in \\mathbb{R},\\\\\n",
    "&3. y = a^x, &&a>0, a \\neq 1, a \\in \\mathbb{R},&& x \\in \\mathbb{R}, &&y \\in \\mathbb{R},\\\\\n",
    "&4. y = \\log_a x, &&a>0, a \\neq 1, a \\in \\mathbb{R}, && x \\in (0, +\\infty), &&y \\in \\mathbb{R},\\\\\n",
    "\\end{aligned}\\\\\n",
    "\\large\n",
    "\\begin{aligned}\n",
    "&5. y=\\sin x,&&y=\\cos x,&&y=\\tan x,&&y=\\csc x,&&y=\\sec x,&&y=\\cot x,\\\\\n",
    "&6. y=\\arcsin x,&&y=\\arccos x,&&y=\\arctan x,&&y=\\operatorname{arccsc}x,&&y=\\operatorname{arcsec}x,&&y=\\operatorname{arccot}x,\\\\\n",
    "\\end{aligned}\\\\\n",
    "$$\n",
    "\n",
    "### 初等函数\n",
    "初等函数是由幂函数（power function）、指数函数（exponential function）、对数函数（logarithmic function）、三角函数（trigonometric function）、反三角函数（inverse trigonometric function）与常数经过有限次的有理运算（加、减、乘、除、有理数次乘方、有理数次开方）及有限次函数复合所产生，并且能用一个解析式表示的函数。\n",
    "\n",
    "### 非初等函数\n",
    "\n",
    "一般说来，大部分分段函数不是初等函数。如符号函数，狄利克雷函数，gamma函数，误差函数，Weierstrass函数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a728f0b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3ea52d6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 绘图的辅助函数\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "def plot_loss_history(loss_history):\n",
    "    \"\"\"Plot the training and testing loss history.\n",
    "\n",
    "    Note:\n",
    "        You need to call ``plt.show()`` to show the figure.\n",
    "\n",
    "    Args:\n",
    "        loss_history: ``LossHistory`` instance. The first variable returned from\n",
    "            ``Model.train()``.\n",
    "    \"\"\"\n",
    "    loss_train = np.sum(loss_history.loss_train, axis=1)\n",
    "    loss_test = np.sum(loss_history.loss_test, axis=1)\n",
    "\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    plt.semilogy(loss_history.steps, loss_train, label=\"Train loss\")\n",
    "    plt.semilogy(loss_history.steps, loss_test, label=\"Test loss\")\n",
    "    for i in range(len(loss_history.metrics_test[0])):\n",
    "        plt.semilogy(\n",
    "            loss_history.steps,\n",
    "            np.array(loss_history.metrics_test)[:, i],\n",
    "            label=\"Test metric\",\n",
    "        )\n",
    "    plt.xlabel(\"Steps\")\n",
    "    plt.ylabel(\"loss\")\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "\n",
    "\n",
    "def _pack_data(train_state):\n",
    "    def merge_values(values):\n",
    "        if values is None:\n",
    "            return None\n",
    "        return np.hstack(values) if isinstance(values, (list, tuple)) else values\n",
    "\n",
    "    y_train = merge_values(train_state.y_train)\n",
    "    y_test = merge_values(train_state.y_test)\n",
    "    best_y = merge_values(train_state.best_y)\n",
    "    best_ystd = merge_values(train_state.best_ystd)\n",
    "    return y_train, y_test, best_y, best_ystd\n",
    "\n",
    "def plot_best_state(train_state):\n",
    "    \"\"\"Plot the best result of the smallest training loss.\n",
    "\n",
    "    This function only works for 1D and 2D problems. For other problems and to better\n",
    "    customize the figure, use ``save_best_state()``.\n",
    "\n",
    "    Note:\n",
    "        You need to call ``plt.show()`` to show the figure.\n",
    "\n",
    "    Args:\n",
    "        train_state: ``TrainState`` instance. The second variable returned from\n",
    "            ``Model.train()``.\n",
    "    \"\"\"\n",
    "    if isinstance(train_state.X_train, (list, tuple)):\n",
    "        print(\n",
    "            \"Error: The network has multiple inputs, and plotting such result han't been implemented.\"\n",
    "        )\n",
    "        return\n",
    "\n",
    "    y_train, y_test, best_y, best_ystd = _pack_data(train_state)\n",
    "    y_dim = best_y.shape[1]\n",
    "\n",
    "    # Regression plot\n",
    "    # 1D\n",
    "    if train_state.X_test.shape[1] == 1:\n",
    "        idx = np.argsort(train_state.X_test[:, 0])\n",
    "        X = train_state.X_test[idx, 0]\n",
    "        plt.figure(figsize=(10, 6))\n",
    "        for i in range(y_dim):\n",
    "            if y_train is not None:\n",
    "                plt.plot(train_state.X_train[:, 0], y_train[:, i], \"ok\", label=\"Train\")\n",
    "            if y_test is not None:\n",
    "                plt.plot(X, y_test[idx, i], \"-k\", label=\"True\")\n",
    "            plt.plot(X, best_y[idx, i], \"--r\", label=\"Prediction\")\n",
    "            if best_ystd is not None:\n",
    "                plt.plot(\n",
    "                    X, best_y[idx, i] + 2 * best_ystd[idx, i], \"-b\", label=\"95% CI\"\n",
    "                )\n",
    "                plt.plot(X, best_y[idx, i] - 2 * best_ystd[idx, i], \"-b\")\n",
    "        plt.xlabel(\"x\")\n",
    "        plt.ylabel(\"y\")\n",
    "        plt.legend()\n",
    "    # 2D\n",
    "    elif train_state.X_test.shape[1] == 2:\n",
    "        for i in range(y_dim):\n",
    "            plt.figure(figsize=(20, 12))\n",
    "            ax = plt.axes(projection=Axes3D.name)\n",
    "            ax.plot3D(\n",
    "                train_state.X_test[:, 0],\n",
    "                train_state.X_test[:, 1],\n",
    "                best_y[:, i],\n",
    "                \".\",\n",
    "            )\n",
    "            ax.set_xlabel(\"$x_1$\")\n",
    "            ax.set_ylabel(\"$x_2$\")\n",
    "            ax.set_zlabel(\"$y_{}$\".format(i + 1))\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28ca0af2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0c795172",
   "metadata": {},
   "source": [
    " “uniform” (equispaced grid), “pseudo” (pseudorandom), “LHS” (Latin hypercube sampling), “Halton” (Halton sequence), “Hammersley” (Hammersley sequence), or “Sobol” (Sobol sequence)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76df54f7",
   "metadata": {},
   "source": [
    "**归一化**\n",
    "\n",
    "$\\large x\\in [0,n],\\to  , 令 y = \\frac{x}{n}，则y \\in [0,1]$\n",
    "\n",
    "$\\large x\\in [a,b],\\to  , 令 y = \\frac{x-a}{b-a}，则y \\in [0,1]$\n",
    "\n",
    "**反归一化**\n",
    "\n",
    "$\\large x\\in [0,1],\\to  , 令 y = n x，则y \\in [0,n]$\n",
    "\n",
    "$\\large x\\in [0,1],\\to  , 令 y = (b-a)x + a，则y \\in [a,b]$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4c5dfe51",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import deepxde as dde\n",
    "import numpy as np\n",
    "\n",
    "# def func(x):\n",
    "#     \"\"\"\n",
    "#     x: array_like, N x D_in\n",
    "#     y: array_like, N x D_out\n",
    "#     \"\"\"\n",
    "#     return scale*x * np.sin(5 *scale* x)\n",
    "\n",
    "def func(x):\n",
    "    \"\"\"\n",
    "    x: array_like, N x D_in\n",
    "    y: array_like, N x D_out\n",
    "    \"\"\"\n",
    "    x = scale*x\n",
    "    return x * np.sin(5 * x)\n",
    "\n",
    "scale = 10\n",
    "# geom = dde.geometry.Interval(-1.2, 1.2)\n",
    "# geom = dde.geometry.Interval(-1., 1.)\n",
    "geom = dde.geometry.Interval(0., 1.)\n",
    "\n",
    "num_train = 200*scale\n",
    "num_test = 100*scale\n",
    "data = dde.data.Function(geom, func, num_train, num_test, train_distribution='pseudo', online=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e4bfebe5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b7c5bd4760>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t_list = data.train_next_batch()[0].flatten()\n",
    "train_data = func(t_list)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(t_list,train_data, label=\"Train_data\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "14216bcf",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b7c5d879d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t_list = data.train_next_batch()[0].flatten()\n",
    "train_data = func(t_list)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(t_list*scale,train_data, label=\"Train_data\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "6d662e93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling model...\n",
      "Building feed-forward neural network...\n",
      "'build' took 0.136226 s\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\Anaconda3\\envs\\py3.8\\lib\\site-packages\\keras\\legacy_tf_layers\\core.py:236: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n",
      "  warnings.warn('`tf.layers.dense` is deprecated and '\n",
      "G:\\Anaconda3\\envs\\py3.8\\lib\\site-packages\\keras\\engine\\base_layer_v1.py:1676: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead.\n",
      "  warnings.warn('`layer.apply` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'compile' took 1.002488 s\n",
      "\n",
      "Initializing variables...\n",
      "Training model...\n",
      "\n",
      "Step      Train loss    Test loss     Test metric   \n",
      "0         [1.68e+01]    [1.69e+01]    [1.00e+00]    \n",
      "1000      [1.65e+01]    [1.65e+01]    [9.88e-01]    \n",
      "2000      [1.65e+01]    [1.65e+01]    [9.88e-01]    \n",
      "3000      [1.65e+01]    [1.65e+01]    [9.89e-01]    \n",
      "4000      [1.67e+01]    [1.68e+01]    [9.97e-01]    \n",
      "5000      [1.67e+01]    [1.68e+01]    [9.98e-01]    \n",
      "6000      [1.67e+01]    [1.69e+01]    [9.99e-01]    \n",
      "7000      [1.67e+01]    [1.69e+01]    [9.99e-01]    \n",
      "8000      [9.35e+00]    [1.00e+01]    [7.70e-01]    \n",
      "9000      [2.16e-01]    [2.44e-01]    [1.20e-01]    \n",
      "10000     [6.89e-02]    [7.58e-02]    [6.70e-02]    \n",
      "11000     [1.19e-01]    [1.24e-01]    [8.56e-02]    \n",
      "12000     [4.99e-02]    [5.09e-02]    [5.49e-02]    \n",
      "13000     [2.14e-02]    [2.20e-02]    [3.61e-02]    \n",
      "14000     [3.27e-03]    [3.72e-03]    [1.48e-02]    \n",
      "15000     [4.37e-02]    [4.55e-02]    [5.19e-02]    \n",
      "\n",
      "Best model at step 14000:\n",
      "  train loss: 3.27e-03\n",
      "  test loss: 3.72e-03\n",
      "  test metric: [1.48e-02]\n",
      "\n",
      "'train' took 44.070213 s\n",
      "\n"
     ]
    }
   ],
   "source": [
    "activation = \"tanh\"\n",
    "initializer = \"Glorot uniform\"\n",
    "net = dde.nn.FNN([1] + [100] * 10 + [1], activation, initializer)\n",
    "\n",
    "model = dde.Model(data, net)\n",
    "model.compile(\"adam\", lr=2e-4, metrics=[\"l2 relative error\"])\n",
    "losshistory, train_state = model.train(epochs=15000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "0113c0f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_data = model.predict(t_list)*scale\n",
    "true_data = func(t_list)*scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "32f25371",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b721cb2f70>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t_list = data.test()[0]\n",
    "\n",
    "pre_data = model.predict(t_list)\n",
    "true_data = func(t_list)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(t_list*scale, pre_data[:,0], \"-r\", label=\"pre_data\")\n",
    "plt.plot(t_list*scale, true_data[:,0], \"-k\", label=\"true_data\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "93a35f41",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss_history(losshistory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbfdf4a0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "af163ba9",
   "metadata": {},
   "source": [
    "## 问题设置\n",
    "\n",
    "我们将用神经网络逼近一个函数：\n",
    "\n",
    "$$y = x \\sin(5 x), \\,\\,\\,\\, x\\in [-1,1]$$\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "源码：\n",
    "\n",
    "https://github.com/lululxvi/deepxde/blob/master/examples/function/func.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "06630670",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using backend: tensorflow.compat.v1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From G:\\Anaconda3\\envs\\py3.8\\lib\\site-packages\\tensorflow\\python\\compat\\v2_compat.py:101: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "non-resource variables are not supported in the long term\n",
      "WARNING:tensorflow:From G:\\Anaconda3\\envs\\py3.8\\lib\\site-packages\\deepxde\\nn\\initializers.py:118: The name tf.keras.initializers.he_normal is deprecated. Please use tf.compat.v1.keras.initializers.he_normal instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Backend supported: tensorflow.compat.v1, tensorflow, pytorch, jax\"\"\"\n",
    "import deepxde as dde\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b6a1f60c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def func(x):\n",
    "    \"\"\"\n",
    "    x: array_like, N x D_in\n",
    "    y: array_like, N x D_out\n",
    "    \"\"\"\n",
    "    return x * np.sin(5 * x)\n",
    "\n",
    "\n",
    "geom = dde.geometry.Interval(-1, 1)\n",
    "num_train = 16\n",
    "num_test = 100\n",
    "data = dde.data.Function(geom, func, num_train, num_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6df87d11",
   "metadata": {},
   "outputs": [],
   "source": [
    "activation = \"tanh\"\n",
    "initializer = \"Glorot uniform\"\n",
    "net = dde.nn.FNN([1] + [20] * 3 + [1], activation, initializer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b4c08c21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling model...\n",
      "Building feed-forward neural network...\n",
      "'build' took 0.068177 s\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\Anaconda3\\envs\\py3.8\\lib\\site-packages\\keras\\legacy_tf_layers\\core.py:236: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n",
      "  warnings.warn('`tf.layers.dense` is deprecated and '\n",
      "G:\\Anaconda3\\envs\\py3.8\\lib\\site-packages\\keras\\engine\\base_layer_v1.py:1676: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead.\n",
      "  warnings.warn('`layer.apply` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'compile' took 0.666008 s\n",
      "\n",
      "Initializing variables...\n",
      "Training model...\n",
      "\n",
      "Step      Train loss    Test loss     Test metric   \n",
      "0         [3.35e-01]    [2.91e-01]    [1.18e+00]    \n",
      "1000      [4.98e-02]    [5.16e-02]    [4.97e-01]    \n",
      "2000      [3.64e-02]    [3.86e-02]    [4.30e-01]    \n",
      "3000      [1.07e-03]    [8.39e-04]    [6.34e-02]    \n",
      "4000      [2.97e-04]    [2.78e-04]    [3.65e-02]    \n",
      "5000      [1.13e-04]    [1.37e-04]    [2.56e-02]    \n",
      "6000      [4.98e-05]    [8.65e-05]    [2.04e-02]    \n",
      "7000      [2.06e-05]    [5.87e-05]    [1.68e-02]    \n",
      "8000      [8.30e-06]    [4.42e-05]    [1.45e-02]    \n",
      "9000      [4.15e-06]    [3.66e-05]    [1.32e-02]    \n",
      "10000     [2.26e-06]    [3.18e-05]    [1.23e-02]    \n",
      "11000     [1.28e-06]    [2.80e-05]    [1.16e-02]    \n",
      "12000     [1.70e-06]    [2.80e-05]    [1.16e-02]    \n",
      "13000     [4.04e-07]    [2.41e-05]    [1.07e-02]    \n",
      "14000     [1.91e-07]    [2.24e-05]    [1.04e-02]    \n",
      "15000     [1.00e-07]    [2.15e-05]    [1.01e-02]    \n",
      "16000     [5.52e-08]    [2.09e-05]    [1.00e-02]    \n",
      "17000     [2.76e-08]    [2.04e-05]    [9.87e-03]    \n",
      "18000     [1.66e-08]    [2.01e-05]    [9.80e-03]    \n",
      "19000     [8.84e-09]    [1.98e-05]    [9.74e-03]    \n",
      "20000     [5.72e-09]    [1.96e-05]    [9.70e-03]    \n",
      "\n",
      "Best model at step 20000:\n",
      "  train loss: 5.72e-09\n",
      "  test loss: 1.96e-05\n",
      "  test metric: [9.70e-03]\n",
      "\n",
      "'train' took 20.880969 s\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = dde.Model(data, net)\n",
    "model.compile(\"adam\", lr=2e-4, metrics=[\"l2 relative error\"])\n",
    "losshistory, train_state = model.train(epochs=20000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "be85a6a9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss_history(losshistory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ebcfdeb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GkvzFF1TJyGB9y5ak+8EYXeKeSprIacx7910uHTmSF554gqs6dHAdJ0/VrFmT6aNG8cSBA6S0bs3uVatcRxIvkLRrF1vq1aPqsWOs+u9/afTAA64j5amI3r354+abmb93L0Pef991HPEDKmkip3D0wAFKPPkktwQF8ZSfnMevXocOxH/0EWVTU9nXpAkHdVJ2OYPU1FRmNGlCrcOHWfTIIzR9/nnXkfJFo9hYZl99Nc88/3y+nxRe/I9KmsgpzLjuOmqlprLvxRcJ8cLx0M5X/X79WPvKK1Q7fpyNDRqQcuSI60jigay13HfffdyWkMDP/fvT4p13XEfKN8YYhgwZQpu0NGa3b48v7tctnkMlTeQkqyZOpO2cOSyLjKT2s8+6jpPvGj/zDAvuvptL9+/nve7d9SUk/zKmd29Gffop9z/3HJ0+/NB1nHxXtWpVXrjsMm5dv56Zr7ziOo74MJU0kWzS0tLYe9ttpBtDtcmTXcdx5orhwxkyYABP/vILL774ous44kHmRUdzw9df80XDhvz3v/91HceZSydOZHtwMBVeeIH9O3e6jiM+SiVNJJv33nuP6EOHWPHEExTzg0Psz2TA4MH07duX3198kdnR0a7jiAdY/uabNBk+nEUlStB9zhyMj4wZeD6Cihfn6Ouvc0laGrN0Dtw8ExgYSIMGDahbty49e/YkKSnpvJ+rb9++fP/99wDcfffd/Pnnn6edd+bMmczLdqT70KFDGTly5Hm/9vlSSRPJsnn9egYNGkTNa6+l+WuvuY7jnDGGj4cO5eVSpbh8+HCWvPGG60ji0MZx46j25JNsCgmh+rJlFMh2ah5/Vf2RR/ijRg2uXrSIhd9+6zqOTypYsCDLly9n1apVFChQgKEnDSacnp5+Xs/7ySefUPsM/xE/uaT169fPycnZVdJEyNwR+q+2bRmRksKHH3zg12sIsisQEsLFS5YQHxJC9aee4q/x411HEgd2bt9O6k03cSQggEIzZ1IiIsJ1JI9RffJkXixZkr6DBpGSkuI6jk+78sor2bBhAzNnzqRNmzbccsst1KtXj/T0dJ544gmaNGlC/fr1+TjrNHfWWgYMGEDt2rXp0qXLiVNEAbRu3Zq/B73/8ccfadSoEZdeeint2rUjLi6OoUOH8r///Y8GDRowZ84cXnjhBd566y0Ali9fTrNmzahfvz7dunVj//79J57zqaee4rLLLuPiiy9mzpw5F7zMOsG6CDD/gw/osHUri9q0oUp4uOs4HqVYRARHZ84kuUULQnr2ZM/KlZS+5BLXsSSfHD9+nG49epAUEMBXI0dSu1kz15E8SqEaNWgxYgSvXXstHw8dygMPPug6Ut5p3frf0268Efr3h6Qk6Nz53/f37Zt52bMHbrjhn/fNnJnjl05LS2Pq1KlcffXVACxcuJBVq1ZRtWpVhg0bRrFixVi0aBHJycm0aNGCDh06sGzZMtatW8fKlSvZuXMntWvX5s477/zH8+7evZt77rmH2bNnU7VqVfbt20fJkiXp168fhQsX5vGsIZimT59+4jG3334777//Pq1atWLQoEG8+OKLJ05An5aWxsKFC5kyZQovvvgi06ZNy/EynorWpInfy0hPJ+DZZ9kbEED9b75xHccjVWzWjN3DhlE2LY3vOnUiOTnZdSTJBzYjgyGdOvH777/zfGwstXv1ch3JI3Xp0oWXatcm6rHHOLRvn+s4PuXYsWM0aNCAqKgowsPDueuuuwC47LLLqFq1KgA///wzI0eOpEGDBjRt2pS9e/fy119/MXv2bG6++WYCAwOpWLEibdu2/dfzz58/n5YtW554rpIlS54xz8GDBzlw4ACtss5A06dPH2bPnn3i/u7duwPQuHFj4uLiLnj5tSZN/N6vTz/NVUeOsOT222nsR2Oinas6d93FlP376f/EEyy67z4+/fRTbRb2cXM7duTRmTOpcNtt9OjRw3Ucj2WM4aa+fanx5JNMuP12uk6a5DpS3jjTmq+wsDPfX7r0Oa05+9vf+6SdrFC2fSKttbz//vt07NjxH/NMmTLlrH+jrLW5+ncsJCQEyDzgIS0XTh2mNWni144fP06h998nsUABGp60Q6r8W+fHH2fQoEHM/fxzfrz5ZtdxJA8teOIJrpw2jdmRkfT64gvXcTxejccfZ12pUlw2ZQo7Nm50HcevdOzYkY8++ojU1FQA1q9fz9GjR2nZsiWjR48mPT2d7du3M2PGjH899vLLL2fWrFls3rwZgH1Za0KLFCnC4cOH/zV/sWLFKFGixIn9zb788ssTa9Xygkqa+LUhQ4bQJTmZbe++S0DBgq7jeIX//Oc/DIuIoMM337DghRdcx5E8sO6rr6j31lusKFyYJsuWYQL0VXFWxlDogw+oYC3z9R+YfHX33XdTu3ZtGjVqRN26dbn33ntJS0ujW7du1KhRg3r16nHfffedskyVKVOGYcOG0b17dy699FJuuukmAK699lrGjRt34sCB7EaMGMETTzxB/fr1Wb58OYMGDcqzZTO+OJp4VFSU/fuoDZHTObB7N9Vr1qRxkyb89NNPruN4laRdu9gSGUn5Y8fYNWECNa67znUkySU74+NJu+giLFBg+XLK1q3rOpJXWVGtGhGbN7Nr0SJqREW5jnNB1qxZQ61atVzH8Dmn+nc1xiyx1v7rF8bpf4+MMVcbY9YZYzYYY54+xf29jTErsi7zjDGXusgpvum3m27i5/37efO551xH8TphZctSbOZMUgICCO7Rg33avOMTkpOT6X7LLQwICuJIbKwK2nmoFBvLLaGhPP3qq66jiA9wVtKMMYHAh0AnoDZwszHm5JHlNgOtrLX1gZeAYfmbUnxV4sqVXD5jBkEVKlD/yitdx/FK5S+7jF0ffUTFtDSmtWlzYn8Q8U42I4OYXr2YN28et3z5JZdkbfaRc1Pq8stp9uyzjB07lt+zDYYqcj5crkm7DNhgrd1krU0BRgNds89grZ1nrd2fdXM+UDmfM4qPWtG7N8WB0p984jqKV6sTHc2Mp5/m9i1bePTRR13HkQsw54Yb+M/48Xzcty89e/Z0HcerPfLII7xTqBB7u3fHF3cpkvzjsqRVArZku52YNe107gKmnu5OY0y0MWaxMWbx7t27cymi+KKNc+fSZuVKll5yCRVPNfiinJOOr77KgMce48sPPmDKI4+4jiPnYdkbb9Bi3DgWVqjA3cOHu47j9QoXLkyrK67gmp07+W3IENdxLohKZu46139PlyXtVAOTnDK9MaYNmSXtqdM9mbV2mLU2ylobVUZjXckZrHrgAQoAkZ9/7jqKz3j99df5ulIl2r77LsuzTski3iF++nQin36ajSEh1F2yhIAgDZ+ZG+qOHMlhY0jx4iOgQ0ND2bt3r4paLrHWsnfvXkJDQ3P8GJefxkSgSrbblYFtJ89kjKkPfAJ0stbuzads4qMSEhK4eeVKXrv+eh7U6W1yTWBgIJfPmsXuSy6hfP/+JDZoQOWmTV3HkrM4vHMnqV26YIHQH3+kSIUKriP5jAJly7KiXTtaT5vG4q++IuqWW1xHOmeVK1cmMTERbZ3KPaGhoVSunPM9t1yWtEVADWNMVWAr0Av4x2+xMSYcGAvcZq1dn/8Rxde8/dZbpBpD16zzrEnuKX7RRewfM4YSXbsS17YtJeLjKVS6tOtYchoZGRn0vvtuaqWk0OvNN2l4qvMyygWpPXw4yVWrsu/JJ8ELS1pwcPCJ0yWJG842d1pr04ABwE/AGuBba+1qY0w/Y0y/rNkGAaWAIcaY5cYYDX4m5233pk3c9cEHvNGqFREREa7j+KSq113Huueeo25SEj/UrUtERAQBAQFERkYSGxvrOp7fi42NJTIykoCAAKoVL84PkyYR/v77NHzsMdfRfFJYZCS/3HgjD27dyrJly1zHES+kwWzFb0xt145Ov/5K3DffEHnjja7j+LShzZrx3wUL2J5tWlhYGMOGDaN3797Ocvmz2NhYoqOjSUpK4kZgKNAuMJBHv/iCW2+91XU8n3Xw4EHCw8Pp2LEj3377res44qFON5itSpr4hYM7d3K8QgV2lilD/Z07XcfxeRERESQkJBAAXAL8mW16XFycu2B+LDIykvj4eBoCc4ElQDugot6TPPf2ffdRaehQoubOpXqLFq7jiAfyyDMOiOSX+dHRlLOW0Jdech3FL2zZkjm6zhvAPKBm1vSEhARXkfxeQkICZYEJwG6gB5CK3pP80LdPH3oC6++7z3UU8TIqaeLzkg4epNakSfxZvDgX33OP6zh+ITw8HID3gGRgIlA823TJfxdVrsxYMnfy7UpmUQO9J/mhVLNm/FGjBlesXMmWFStcxxEvopImPu/TESOIzsggNSYGzKmG55PcFhMTQ1hYGFuA7kAk8C3w4vPPO83lr6y1hFepwkagL/BH1vSwsDBiYmLcBfMjFT/4gKLAH9HRrqOIF1FJE5+WkpLCm2+9xdErruDS/v1dx/EbvXv3ZtiwYURERDDPGB4vXJj2QIW339bAmA68/vLL/DpvHhN79GBhRATGGCIiInQgRz4q36EDy6tU4fIFC9i1aZPrOOIlVNLEp8197jnu37KF5x5+2HUUv9O7d2/i4uLIyMhg8OHDTGvblufWrOGNN95wHc2vzHvqKa4bNIgHunblu+++O/GexMXFqaDls5Jvv80w4BOdlUNySEd3ik9bXKwYFZOSKH/0KAEFCriO49estdxyyy188803TB4+nE533eU6ks9bHRtL5K23El+oENW2bCG0RAnXkfzeNddcw+LFi4mPjyckJMR1HPEQOrpT/M7Kr74i6tAh4jp3VkHzAMYYPvvsM94ID6fF3Xfzp8aMylPbliyhxO23czAwkLK//66C5iEeHDCAJjt3MuOVV1xHES+gNWnis2bUqEHTDRtI37yZIpGRruNIll1Ll2KbNCHZGIKXLKHCpZe6juRzju7Zw6aICKomJbHju++ofsMNriNJlozkZHYVLkx8WBhNDx50HUc8hNakiV/Z+eefNNuwgeV16qigeZiyjRpx6MsvKZOezu7mzTmswYVzVVpaGv1uvZUjSUmsfe45FTQPExASQlynTjQ9dIgVX3/tOo54OJU08UlfDR/OOKDiq6+6jiKnUOOWW/jz2Wepm5TEivr1SU1JcR3JJ1hrefD++xn1008sHTyYKA3e7JHqDh7MMWD3oEGuo4iHU0kTn5OSksIbo0czqlMnIq+91nUcOY3GMTHMv+UWPtq1i+h779XQHLlg2jXXcO2wYTz36KPc/8ADruPIaRSOjGR5nTpcvmEDO9escR1HPJhKmvicX994g1I7dvCAvqQ8XvPYWKr/5z988cUXvP3II67jeLVZAwbQbsoUSlWpwouvveY6jpxFxddeYycw8X//cx1FPJgOHBCfs6xoUUofO0alY8cICApyHUfOwlrLq9dey6OTJ/P7HXfQ5rPPXEfyOovefpv6jz/O+mLFqJmQQIGiRV1Hkhy4pnNnlixbRnx8PAV0BLpf04ED4hdWjxpFw8OHibvmGhU0L2GM4YnRo1lbsiRXfP45819+2XUkr7JmzBiqP/44W0NCiFi+XAXNiwx48EH27djBlI8+ch1FPJTWpIlPmV29Oo02biQjPp6iOnG0VzmydSvba9Sg/LFjbBgyhIb33ec6ksdbt24d/Zo354PDhyk1ezblmzVzHUnOQUZGBssLFcIEB9Pw0CHXccQhrUkTn7dr1SqabtzI0nr1VNC8UOFKlSixYAH7goOJ7N+fZePGuY7k0eJWrKBd27asDgwkcPlyFTQvFBAQwNGrr6bh4cOs+vJL13HEA6mkideLjY0lMjKSPvXqkQL81bGj60hynkrXq0fInDl8UaIEbe+4g+XLl7uO5JG2LV9OWlQUA/btY9q0aVxSu7brSHKeLn3vPY4Ci++8k4CAACIjI4mNjXUdSzyESpp4tdjYWKKjo4mPj+dHoBzw4JAh+iPnxco3bUq3pUspUrQo97Rty4apU11H8ii71q7lULNmVExNpet771G/fn3XkeQC/DBnDqON4Ya0NApbS3x8PNHR0fobJoD2SRMvFxkZSXx8PGFAUrbpERERxMXFOUoluWH92rWk1KtHiYwM0mbMIKJlS9eRnNsXF8fW2rW5+Ngx1r/9NvUefdR1JLlAkZGRlI2PZyFwK/B3NdPfMP+ifdLEJyUkJADwKTD9FNPFe118ySWEfvUVhayFdu2ImznTdSSn9u7Zw8b69al17BhrXnxRBc1HJCQksAhowv8XtL+ni6ikiVcLDw+nFNANWHnSdPF+1Xv2ZOeIERRLTye4XTvWT5zoOpIT27Zto2WrVrx/7BgrnnqKBjqdkM/4+2/Vydt+9DdMQCVNvFxMTAx3BAQQAgzPmhYWFkZMTIzLWJKLat52G3vHjCEYWHfDDSxcuNB1pHyV8PvvPNe4MQkJCdzx88800tkEfEpMTAxhYWEAvAG8jv6Gyf9TSROvdsvNN3NvQAC/G8OfxhAREcGwYcPo3bu362iSiy7q1o3k6dN5rlIl2rVrx8wZM1xHyhd//fQT9soreXPnTn6dMIE2bdq4jiS5rHfv3gwbNoyIiAhKA/cBQ996S3/DBFBJEy+3ZvhwqqelkXzbbWRkZBAXF6c/bj6qSuvWTP3tNy6uXJmUdu1Y8MILriPlqdXffkuRTp0okpHB/thYmrRt6zqS5JHevXsTFxdHvcGDKQLUWLrUdSTxECpp4tU+XLCAuwsU0CYgP1GxYkV+Hj+e8IIFafTii8y8+WZ88Qj13196iYo33YQJCODI5MlUv/lm15EkHzTq35/1wcEU/eYb11HEQ6ikidc6cuQII7/7jvRbbqFohQqu40g+KVWzJhXXruWPMmVoPXo0s+vUIeXIEdexcoW1lpiYGGYOGsS+0FDsnDmEd+rkOpbkk4DAQOI7dqT24cPET5jgOo54AJU08VqLHnuMe48c4Z4773QdRfJZ0SpVaJiYyKxmzWi1Zg3TLrqIXbt2uY51QY7u3csjnTrx3HPPsbpXLyomJFD+8stdx5J8Vve11xhqDN9MmeI6ingADWYr3slaNhQuzPGMDOokJWGMcZ1IHJn34IPcP2wYe8uWZcKECTRs2NB1pHOWuHAh+9q0oXRSEt++/DIPPfusfqf9WNeuXVmwYAFbtmwhODjYdRzJBxrMVnzKptGjqZ6UxPZrrtGXmZ9rPngwn86bB9ayqEkTZt94Ixlpaa5j5di8558n6PLLqZaUxI7nnuPhgQP1O+3n7r7rLiJ37mT+m2+6jiKOaU2aeKXfL72U+itWcGzjRkpXq+Y6jniAnZs3k9CkCU327mV58eKUnjiRylde6TrWae3bsYMVLVrQetMmNoSGEvjNN1S97jrXscQDpKWmElewIMnFilFn717XcSQfaE2a+Iy0I0eovXIliypXVkGTE8pVrUrUrl3Mvv12qh04QPGWLZnbuzejRo4kMjKSgIAAIiMjnZy4OjY29h8ZHn/8ceo0aMCBzZuZc8UVROzerYImJwQFB7O+eXPq7NvH3kWLXMcRh1TSxOv8Nm4c860lUAcMyElMQAAtR4zg4Ny5rC1RgjpffcXjffsSHx+PtZb4+Hiio6PztajFxsYSHR1NfHw8pazlifh4xr79NsEFChCxaBFXzplDcOHC+ZZHvEPV558nA9j4n/+4jiIOaXOneJ1bb72VyZMns2PHDkJCQlzHEQ+VkZ7OZaVKseTgQQKAGDJPHbYJiIiIIC4uLl9yREZGcig+nseAh4CCQDQwLTyc+Pj4fMkg3un3IkUIT02lUlISBGidii/T5k7xCUcSE/l9zBhuvPFGFTQ5o4DAQJYeOgRAPTIL0jrgYyAjn8rRoUOHuCk+ns3AQGASUAf4DNiyZUu+ZBDvtf/aaymYnMym6dNdRxFHnJY0Y8zVxph1xpgNxpinT3G/McYMzrp/hTGmkYuc4jnWPPUU648f5+727V1HES8QHh4OwB9ANWAI0Af4C1hQvTpzfviB9PT0XH1Nm5HBmhEjeOyRR6hWrRoVgV+B+sDNZBbF7NlETqfhq69SyRg+mzXLdRRxxFlJM8YEAh8CnYDawM3GmNonzdYJqJF1iQY+yteQ4nGK//ADfxYoQFSPHq6jiBeIiYkhLCwMgB1krk2rFxLC5IoVCdm8mVbXXUd4eDhfdunCuo8/xp7v0B3WsnniROZceSWJISHU6tuXZR98QMuWLTn0wgvcGhbGymyzh4WFERMTc6GLJz6uQkQELdu3Z9SXX5KRmuo6jjgQ5PC1LwM2WGs3ARhjRgNdgT+zzdMVGGkzd5ybb4wpboypYK3dnv9xxbWd06ZR4/BhpnTsSD2NIyU50Lt3bwAGDhxIQkIC4eHh/Ccmhu69e5N09CijJ0/mq9hYWkycSLUpUzjWrx8JhQuzv0oVjrZsSeE77qBihQoEb9z4/09qDIfj4li9cye/7dnDnt9/5+1586hqLVWApSVLsrlrV8b85z+UiIgAoFr16v/IEBMTcyKbyJnc27kz9X7+mXX//S+1XnrJdRzJZ84OHDDG3ABcba29O+v2bUBTa+2AbPNMAl6z1s7Nuj0deMpae8ajAnTggG9a0Lo1jWbNYsv8+VRr2tR1HPEh+xMTWRYTQ9pvv1EsIYGIQ4cYay33AwbIOMVj3gSeDwmh4SWX8MqxY9CoEbWeeYby9evnb3jxaUcPH2ZPsWIcrlSJutqP0Wed7sABl2vSTrUq5OTGmJN5Mmc0JprMTaLa18MH2YwMwufN4/cSJWipgia5rETlyrT96P/3psjIyODqv/5i3Jo17Nm9m1///k9f1n9qg0uW5PrOnXmkeXOCglz+GRVfV6hIEabVrcs1K1dyfNMmQjU2pF9x+dclEaiS7XZlYNt5zAOAtXYYMAwy16TlXkzxBH+sWEGX1FTefOAB11HEDwQEBFCtZk2q1ayZOeGee9wGEr9W+tFHCbzjDlYPGkT9UaNcx5F85PLozkVADWNMVWNMAaAXMPGkeSYCt2cd5dkMOKj90fzTl19+ye7gYDo++KDrKCIi+arZbbexODiY4hMmnFibK/7BWUmz1qYBA4CfgDXAt9ba1caYfsaYflmzTSFz7MkNZI5D2d9JWHEqbd8+On74IQ81a0apUqVcxxERyVeBgYEs79qVx5OS2L1rl+s4ko+cjpNmrZ1irb3YWnuRtTYma9pQa+3QrOvWWnt/1v31znbAgPimNS+/TIfkZDp36uQ6ioiIE00HDeK7jAxGf/ut6yiSj3TGAfF45uuv2RgQQPNHHnEdRUTEiXr16tGhVi1480043/H8xOuopIlHO7Z5M7V27ODP+vUJCQ11HUdExJl+UVE8sGULO776ynUUyScqaeLR1r/8MoFAqQEDzjqviIgvazRwIAeBXYMHu44i+UQlTTzazNWrGRsSwmV9+riOIiLiVETNmswpU4aqy5bB8eOu40g+UEkTj3X48GGeWbGC6XfdpQFDRUSA1B49KJKRQeLw4a6jSD5QSROPNevjjzHHjtGrVy/XUUREPELTZ54hEVg78eRhRcUXafWEeKzaL73EzwUKcHmLFq6jiIh4hIrh4XRo2ZItiYn8aS3GnOrsieIrtCZNPNLB+fOpdugQu5o3JyBAv6YiIn/rccstrF27lhVLlriOInlM337ikTa9+irpQOSTT7qOIiLiUXr06MFXxhCiXUF8nkqaeB5rKTNtGvNDQ2lw9dWu04iIeJTSpUsTGBHBRRs3Ynfvdh1H8pBKmnicfbNmUTkpiZ1t2mh/CxGRUwi94w6Cgbi333YdRfKQSpp4nG/XrKEBcPHAga6jiIh4pJYPPMBaIGPUKNdRJA+ppInHGf3NN6TUqkWd5s1dRxER8UjFS5Rg6SWXUHXrVjISElzHkTyikiYeZfcPP9Bn1izu6txZmzpFRM6gcL9+PAjMX73adRTJIypp4lG2vfEGNwHX3Hyz6ygiIh6t7V138VnBgsROmuQ6iuQRlTTxHOnpVF6wgN+KFaNm48au04iIeLTChQtz49VXEzxyJOlxca7jSB5QSROPsWvsWEqlpnKkUyfXUUREvMKN7drx7pEjxL32musokgdU0sRjbH//fY4BdTWArYhIjrTq25clxhA4bpzrKJIHVNLEYyxKSGBCyZLUaNjQdRQREa9QqFAh/qxdm8hdu8jYvNl1HMllKmniEbZv3050QgLrHnzQdRQREa9S/O67AYh/5x3HSSS3qaSJR5j2ySdgLTfccIPrKCIiXqXVnXeyzBh2/fqr6yiSy1TSxL2MDDrGxDC2aFFq167tOo2IiFcpWrQor3bqxA2HDpGRkeE6juQilTRxbv+PP1I2OZk0natTROS8XNurF4mJiSxauNB1FMlFKmni3JZ33yUZqPn4466jiIh4pWuvvZY3AwIo0auX6yiSi1TSxC1rKTdnDr+FhVG3RQvXaUREvFLx4sUpW706F8fHYxMTXceRXKKSJk4dnDaNcsePs1ebOkVELkjhPn0A2PLee46TSG5RSROnxsfF0RGooU2dIiIXpHW/fqwC0kePdh1FcolKmjj17fjxrI+M5NJWrVxHERHxaiVLlmTpRRcRkZiI3bbNdRzJBSpp4syh336j5Y8/0qdTJ23qFBHJBaF9+xID/Ll2resokgtU0sSZhNde47GMDK69/nrXUUREfEKbe+/lhYAAvpk503UUyQUqaeKGtZScMYN5ISE0at/edRoREZ9QpkwZ2rdsya7PP4e9e13HkQukkiZOHF24kIpHj7KjeXNt6hQRyUV3NGvG0MREtg8Z4jqKXCCVNHFi8zvvkAFUGTDAdRQREZ9yxf33sxk4EhvrOopcIJU0cWLDqlXMCg7msq5dXUcREfEplSpXZn758kSsXw+HD7uOIxdAJU3yXXJyMn0SExl1660EBga6jiMi4nMyunalgLXs+fJL11HkAqikSb6bOWUKhw4donuPHq6jiIj4pCYPPcQuYLs2eXo1JyXNGFPSGPOLMeavrJ8lTjFPFWPMDGPMGmPMamPMQy6ySu6r2r8/Y4KCaNeunesoIiI+6eJatbi1Rg0eCg52HUUugKs1aU8D0621NYDpWbdPlgY8Zq2tBTQD7jfG1M7HjJIH0nfs4KIdO8i4+GJCQ0NdxxER8VlNb7qJWXPmsGfPHtdR5Dy5KmldgRFZ10cA1588g7V2u7V2adb1w8AaoFJ+BZS8senddwkEitx2m+soIiI+rVu3brySkcHWO+5wHUXOk7HW5v+LGnPAWls82+391tp/bfLMdn8kMBuoa609dJp5ooFogPDw8Mbx8fG5mllyx+pq1Si0eTMlDxygaLFiruOIiPgsay2TCxemRVoaJZKSQAdqeSxjzBJrbdTJ0/NsTZoxZpoxZtUpLuc05oIxpjAwBnj4dAUNwFo7zFobZa2NKlOmzIXGlzxgDx2i+ubNLI+MVEETEcljxhj2tWpFiZQUkn791XUcOQ95VtKstVdZa+ue4jIB2GmMqQCQ9XPXqZ7DGBNMZkGLtdaOzauskj9WrF5NNJDep4/rKCIifuGiBx4gGdgyeLDrKHIeXO2TNhH4+5u6DzDh5BlM5rmCPgXWWGvfycdskkfG/vgjowICuLJ/f9dRRET8QrMOHZhToADFZ84EB7s3yYVxVdJeA9obY/4C2mfdxhhT0RgzJWueFsBtQFtjzPKsS2c3ceWCJScTMnw41112GWXLlnWdRkTELwQGBrKxeXN+TE4m+cAB13HkHAW5eFFr7V7gX4NkWWu3AZ2zrs8FdOZtH7Ft1Cie3b6d8ddc4zqKiIhfqfz441wzcyZl58+nU6dOruPIOdAZByRf7B42jMNAw8cfdx1FRMSvtGvXjiKFCzP/009dR5FzpJImeS89nSrLlvF78eJEXHyx6zQiIn4lNDSU/118MS+OGUP6xo2u48g5UEmTPLdn8mRKpqZy5KqrXEcREfFL5bMGEI//4APHSeRcqKRJnlv79dccBy555BHXUURE/NKVd97JGiB9rEaz8iYqaZLnXjl4kKaRkdS6/HLXUURE/FLRokVZUbUqkQkJ2P37XceRHFJJkzx1+PBhpk+fzlXdu5M59J2IiLhQ4IYbCAa26gACr6GSJnlqw4MPMjElhW467FtExKmmDzzAdcDXR464jiI5pJImeSps8mQqBgZyeZs2rqOIiPi1ilWqsPOyy/huypSzzyweQSVN8kzqtm3U2L2bTfXqERgY6DqOiIjfu6ljR65etIhdP/zgOorkgEqa5Jn177xDAFD89ttdRxEREaDz9dfzDLDjHZ0S2xuopEmeSRs7li3G0CQ62nUUEREBajZsyLxChSg7f75OuO4FVNIkT1hrGbNvHz/XqUNYoUKu44iICGCMYd8VV1D++HEO//ab6zhyFippkieWLl3KSwcPEvjYY66jiIhINhH3308GEDd4sOsochYqaZInFn70EUWN4ZprrnEdRUREsmncuTO/BwcT/8cfrqPIWaikSe7LyODGESMYU6YMpUuXdp1GRESyCQwM5Ivbb+eW7dtJTk52HUfO4KwlzRgzwBhTIj/CiG9IHDOGUmlppHTo4DqKiIicQtfrr+fw4cPMmjbNdRQ5g5ysSSsPLDLGfGuMudro3D5yFts++ohUoLb2RxMR8Ujt2rXj+8BAKvTv7zqKnMFZS5q19jmgBvAp0Bf4yxjzijHmojzOJl6q7IIFLClcmMgGDVxHERGRUyhYsCAFqlbl4oQEMg4edB1HTiNH+6RZay2wI+uSBpQAvjfGvJGH2cQL7Vu0iMikJPa2aOE6ioiInEFIz56EABs/+sh1FDmNnOyT9qAxZgnwBvAbUM9aex/QGOiRx/nEy0xctYq6QKUnnnAdRUREziDqoYfYDxz6+mvXUeQ0gnIwT2mgu7U2PvtEa22GMUbjK8g/TJg4kYOVK3Np27auo4iIyBmULFeO6WXL0mD1akhPB51j2ePkZJ+0QScXtGz3rcn9SOKtkhIT6f7DD/Rr0QIdXyIi4vn29ezJwPR0Nqxd6zqKnILGSZNcs+add7gtPZ2rmjd3HUVERHIg6rHH+BiY8OOPrqPIKaikSa5JHz+encbQUCdUFxHxClWrVqV1rVoc+uQT11HkFFTSJFekHztGzbg4VletSoHQUNdxREQkh56qXJkX165l39KlrqPISVTSJFesGTaMYtYS3K2b6ygiInIOKvfrB8D6d95xnEROlpOjO0XOaumMGQQC9R991HUUERE5B3W6dWNjUBAhv/ziOoqcRGvSJFfErFnDQ+3bU6xiRddRRETkHBhj2FynDrV37eLYrl2u40g2KmlywdYtW8aG9evp2rWr6ygiInIeit16KyHAH0OHuo4i2aikyQWJjY3lm+bN2QYMjokhNjbWdSQRETlHl/bvT43QUNq/+SYBAQFERkbq77kH0D5pct5iY2OJjo7ml+PHSQDWb99OdNbwG71793YbTkREcuy7cePYnJpK+vHjAMTHx+vvuQcwmedO9y1RUVF28eLFrmP4vMjISJLi49kBvAC8lDU9IiKCuLg4Z7lEROTcREZGEhAfz/+AGGBR1nT9Pc8fxpgl1tqok6drc6ect4SEBLqQ+Us08aTpIiLiPRISEtgPdAGuP2m6uKOSJuctPDyc64AE4I+TpouIiPcIDw/nADAHuPak6eKOSpqct+eff573gMezTQsLCyMmJsZVJBEROQ8xMTGEhYXxA1APiEB/zz2BDhyQ81aiRAlmAeXKlcPs2kV4eDgxMTHayVRExMv8/Xd76GOPwc6d3FiwIJcOG6a/5445OXDAGFMS+AaIBOKAG621+08zbyCwGNhqrb0mJ8+vAwfyx3tt2vDDkiX8uG8fQUHq+yIivuDXEiWYW7o0g/76y3UUv+FpBw48DUy31tYApmfdPp2HgDX5kkpyLC01lZ6zZvFqiRIqaCIiPuS3Rx/lhY0b2bFjh+sofs9VSesKjMi6PoJ/HkxygjGmMpkHm3ySP7Ekp5Z//jkVrcVce+3ZZxYREa9x/fXXE2It0zSYrXOuSlo5a+12gKyfZU8z37vAk0DG2Z7QGBNtjFlsjFm8e/fuXAsqp7bns8/IAC557DHXUUREJBfVrVOHjYGBlP/f/1xH8Xt5VtKMMdOMMatOccnRCR6NMdcAu6y1S3Iyv7V2mLU2ylobVaZMmQvKLmdmraXysmWsLVGCwlWruo4jIiK5yAQEsL1GDS7dupXDBw64juPX8qykWWuvstbWPcVlArDTGFMBIOvnrlM8RQvgOmNMHDAaaGuMGZVXeSXnVs2dS3hKCkfatHEdRURE8kDBnj0pAyz+8EPXUfyaq82dE4E+Wdf7ABNOnsFa+4y1trK1NhLoBfxqrb01/yLK6YybMYOyQOSbb7qOIiIieeDiBx8kFTjy9deuo/g1VyXtNaC9MeYvoH3WbYwxFY0xUxxlkhwaP348jZs3p2y1aq6jiIhIHggqXZq/ypen+po1pKamuo7jt5yUNGvtXmttO2ttjayf+7Kmb7PWdj7F/DNzOkaa5K0ta9YwfNkyHq1Z03UUERHJQ7sfeojbMzKYM2eO6yh+S6eFknOy4u23aQxc1rq16ygiIpKHoh54gFWhoYwfP951FL+lkibnJHDKFA4GBFDl5ptdRxERkTxUqFAhHm7cmJJffomLsxOJSpqcg/179tBo+3Y2X3IJBAe7jiMiInnslsKFefbAAVbMnes6il9SSZMcW/jBB5QFCmktmoiIX6jSvz8FgHXvv+86il/SSRclx6bPnUtKaChd+vd3HUVERPJB8c6dORQURMFp01xH8UtakyY5cvz4cT5asIDJffoQULKk6zgiIpIfgoJIrF+fZvv3E7dxo+s0fkclTXJkzvjxlD5yhK5dc3RWLxER8RElb7uNNGDGF1+4juJ3VNIkR/a+9x6bgbbVq7uOIiIi+aj8gAF0qF2bERovLd+ppMlZpaenU3nJEhKKFSOkRg3XcUREJD8FBXF99+7MmT2b3bt3u07jV1TS5KwWTp1Ks9RUDrdt6zqKiIg4cHuVKsRby8xPPnEdxa+opMlZbf7gA4KAyAcfdB1FREQcqN6+PZWBg6NGuY7iV1TS5IystZSYM4e9ISEUatnSdRwREXHAVK3K1tKlqblmDYcPH3Ydx2+opMkZ/fHHH9yelMS8Rx+FAP26iIj4q7TOnWluLb9++63rKH5D37pyRuPGjWNfQADNHnnEdRQREXGo8n33EQhs035p+UYlTc6o4PDhvFK9OmXKlHEdRUREHAq87DJ+qVOHb1esIDk52XUcv6CSJqe18a+/6LN9O90KFnQdRUREXAsIIOX115mZlMSvv/7qOo1fUEmT05o/eDAVgBJ9+7qOIiIiHqBd27Y0L1iQ3z7/3HUUv6CSJqeVMW4caUCZPn1cRxEREQ8QagzTUlOp8cMPpKenu47j81TS5JR27NhB461b2VK1KpQo4TqOiIh4gtBQ9jRqxFXHj/P7b7+5TuPzVNLklCZ/8w37gZBevVxHERERD1L6zjupBCz++GPXUXyeSpqc0ndTp9K3enUqvPyy6ygiIuJBCt5wA+lA4JQpWGtdx/FpKmnyLwcPHmTe9Ol069YNowFsRUQku1Kl2FWjBk0PHGDFihWu0/g0fQPLv0z7+mt2pqURHRLiOoqIiHig4FGjaAuMHTvWdRSfppIm/7J9+HAKAtWuu851FBER8UClL7uMxi1bqqTlMZU0+YcjR44QuXw5+woXJiAqynUcERHxUM9XrEj/VatYu3at6yg+SyVN/uGXsWO5KiODpI4dwRjXcURExENdVrYs0cDUkSNdR/FZKmnyD/EffUQoUGHAANdRRETEgxXt25dA4NCoUa6j+CyVNDkhKSmJj//4gzHNmhF45ZWu44iIiCdr0ICDJUvSeMsWNmzY4DqNT1JJkxN++ukn1h47RvGXX4bAQNdxRETEkxmD6d6d9sAPsbGu0/gklTQ5YemwYfQtXJhWzZu7jiIiIl6g6J13sqhYMWaNGeM6ik9SSRMAkpOTuWTaND5ISSFIa9FERCQnLr+cec8+y4SVK4mPj3edxueopAkA06ZOpVNaGgeuvBIKFHAdR0REvESPHj2oAIwfPdp1FJ+jkiYArB4yhJJA2X79XEcREREvctHu3WwDtn3+uesoPkclTUhJSaHUrFkkBwUR3KWL6zgiIuJNGjfmWGgotdetIzEx0XUan6KSJvz666/UTElhb9OmULCg6zgiIuJNgoNJ6diR64Dx333nOo1PcVLSjDEljTG/GGP+yvpZ4jTzFTfGfG+MWWuMWWOMuTy/s/qDMWPG0LlwYUqNH+86ioiIeKFid9xBCWCzNnnmKldr0p4GpltrawDTs26fynvAj9baS4BLgTX5lM9vpKWlMW7sWK697jpCSpd2HUdERLxRhw6kBAdTY+VKduzY4TqNz3BV0roCI7KujwCuP3kGY0xRoCXwKYC1NsVaeyCf8vmNWTNnMnXfPp7UZk4RETlfBQuyffBg/guMGzfOdRqf4aqklbPWbgfI+ln2FPNUA3YDnxtjlhljPjHGFMrPkP5g/kcf0QS4pHFj11FERMSLhd97L8UuuYRvv/3WdRSfkWclzRgzzRiz6hSXrjl8iiCgEfCRtbYhcJTTbxbFGBNtjFlsjFm8e/fuXFgC35eamkrBqVNJN4aQnj1dxxERES9mjCHmkkuoNnMm27dvdx3HJ+RZSbPWXmWtrXuKywRgpzGmAkDWz12neIpEINFauyDr9vdklrbTvd4wa22UtTaqTJkyub04Pmn6tGlcd+wY++rVA+2PJiIiF6j9gQMMAr775hvXUXyCq82dE4E+Wdf7ABNOnsFauwPYYoypmTWpHfBn/sTzD/M//JDqQPH77nMdRUREfECRu+4iAlj16aeuo/gEVyXtNaC9MeYvoH3WbYwxFY0xU7LN9wAQa4xZATQAXsnvoL7q+PHjTJg1i2m1ahF8442u44iIiC+47jpSg4Kou2oVcXFxrtN4PSclzVq711rbzlpbI+vnvqzp26y1nbPNtzxrE2Z9a+311tr9LvL6oqlTp7L8yBEy3n0XSpZ0HUdERHxB0aKkXHUVNwLf6VyeF0xnHPBTs4YPp0uxYrRt3dp1FBER8SGF7rqLA2FhTB81ynUUr6eS5oeOHDlCzZ9/ZuyRIwQlJ7uOIyIivqRHD36MieGn1atZt26d6zReTSXND00aP57u6ekcvPJKKFLEdRwREfElxnDjjTdSGPjuq69cp/FqKml+aPWQIZQDSvXv7zqKiIj4oIrbt7M7IIDEzz7DWus6jtdSSfMz+/fvJ2L+fI4HBxNwzTWu44iIiC+qVw8TGkqLxERWrFjhOo3XUknzM+PHjaOVtRxt1w50vk4REckLBQpgu3XjeuD7L790ncZrqaT5ma9Hj+baqlUp+cUXrqOIiIgPC+3blyLAnpEjtcnzPKmk+ZFdu3Yxffp0etx8M6ZcOddxRETEl7VuzbGiRWm7ezcLFy50ncYrqaT5kXFffcWijAyiy5Z1HUVERHxdUBAZQ4fyWnAwozWw7XlRSfMj8R9/TCMgok4d11FERMQPFLr5ZiKuuYbRo0eTlpbmOo7XUUnzExs3bqTh2rUcLVQIdJYBERHJJw83akSvHTuYPn266yheRyXNT3z38cdcB2TcdBMEBbmOIyIifqL5/v28AYz7+GPXUbyOSpofyMjIIOnTTwkBijzwgOs4IiLiR4LuvptgoNgPP3Do0CHXcbyKSpofmDt3LrP27WNNx47QoIHrOCIi4k9q1eJInTrcmpbGd99+6zqNV1FJ8wMjR45kaeHChI8Z4zqKiIj4oUIDBlAPWDBkiOsoXkUlzccdO3aMvV99xX0dO1KoUCHXcURExA+Zm29mX8mS7Fy2jM2bN7uO4zVU0nzchO+/Z+ixYzyxY4frKCIi4q+KFePI0qX8YAxf6jRROaaS5uPWvvsu5YBSjz/uOoqIiPix8IgI2rVqxeTPPtNponJIJc2Hbdu2jfpLl3KkUCECunRxHUdERPxcbGIi/42PZ968ea6jeAWVNB82bvhwrgFSevaE4GDXcURExM8V79GD9sAPOoAgR1TSfJS1ltWff44xhpKPPuo6joiICAWiowkACo0Zw/Hjx13H8XgqaT5q+fLlfBQfz8g334R69VzHERERgWrV2H/ppfRKTmbihAmu03g8lTQf9eUXX1CgQAG63XGH6ygiIiInFHv4YWoAi997z3UUj6eS5oOSk5O5eNgwFhcrRsnixV3HEREROSGgZ08+69WLdxcsYOvWra7jeDSVNB80YfRoeh4/TplLLoEAvcUiIuJBChWi1csvk5qRwaeffuo6jUfTN7gP2vzKK5QCyr74ousoIiIi/3JRRATfREZy9N13SU9Pdx3HY6mk+Zi1a9bQYf16dpcrR0Dr1q7jiIiI/FtQEG1DQ4nev5+pkya5TuOxVNJ8zM//+Q8NgZDHHwdjXMcRERE5peLPP89FwNKXX3YdxWOppPmQY8eO8fYvv/Bpw4YUve8+13FEREROK6hnTw4WKULzxYtJSEhwHccjqaT5kO+//56EAweo9vbbUKiQ6zgiIiKnFxxMRr9+XAVMeOUV12k8kkqaD9n+wgs8W64crbUvmoiIeIESTz7J9EqV+GbsWFJTU13H8TgqaT5i9dKl9Nm0iTtLlsRoXzQREfEGpUtzdMgQftu9m0k6gOBfVNJ8xOKnn6YcUFrDboiIiBfp3LkzV5Uty+KYGNdRPI5Kmg84evQodaZPZ1vRohTr0cN1HBERkRwLCgpieMGC3LNkCZvWr3cdx6OopPmA6a++SlRGBkl33KEzDIiIiNcpPHAgkcBvTz/tOopH0Te6D/hhzBjmh4Vx0X//6zqKiIjIOSt9xx3sLFiQapMmkZyc7DqOx3BS0owxJY0xvxhj/sr6WeI08z1ijFltjFlljPnaGBOa31k93bx58/hk7VoWv/46pmhR13FERETOXVAQB3r3pkVqKtM0HMcJrtakPQ1Mt9bWAKZn3f4HY0wl4EEgylpbFwgEeuVrSi8w47HHqFq8OHfccYfrKCIiIuft4rffZltwML9/+inWWtdxPIKrktYVGJF1fQRw/WnmCwIKGmOCgDBgW95H8w6xsbFElS/P4/Pn83RSEuPHj3cdSURE5LyZokV5uU8fYrZuJSAggMjISGJjY13HcirI0euWs9ZuB7DWbjfGlD15BmvtVmPMW0ACcAz42Vr7cz7n9EixsbFER0fzelISAcArKSnsjo4GoHfv3m7DiYiInIfY2Fi+yCplUcDi+Hii/fy7zeTVKkVjzDSg/CnuGgiMsNYWzzbvfmvtP/ZLy9pPbQxwE3AA+A743lo76jSvFw1EA4SHhzeOj4/PhaXwTJGRkaTGx7MJGEnWQgMRERHExcW5CyYiInKeIiMjiY+Ppw/wBXA5MB//+G4zxiyx1kb9a7qL7b7GmHVA66y1aBWAmdbamifN0xO42lp7V9bt24Fm1tr+Z3v+qKgou3jx4ryI7hECAgJ411r6AzWAuKzpxhgyMjLcBRMRETlPAQEBWGspBGwGlgJX4x/fbacraa72SZsI9Mm63geYcIp5EoBmxpgwk3meo3bAmnzK59EqV6pEBTJ35ovLNj08PNxNIBERkQv093fYUeANoCOZa9P8+bvNVUl7DWhvjPkLaJ91G2NMRWPMFABr7QLgezLL9MqsrMPcxPUs7a66ihuBe7NNCwsLI0an1BARES8VExNDWFgYAEOAncB/jfHr7zYnmzvzmi9v7kzfsYMOzZoRHxREamoqW7ZsITw8nJiYGL/dsVJERHxDbGwsAwcOJCEhgaeCg3kwJYWw9espVqOG62h5ytM2d8p52njvvUyOj+ftZ54hPj6ejIwM4uLiVNBERMTr9e7dm7i4ODIyMrhp1iyqAUPHjnUdyxmtSfMmiYkci4hgalgY1+3fT1CQqxFURERE8t5VV13FhtWrWTd9OiG1a7uOk2e0Js0H7LjtNkxGBocfe0wFTUREfN6TTz7Jhzt2cKxNG/DDc3qqpHkJ++uvlJ85k4+KFqXXM8+4jiMiIpLn2rdvz8w6dSi+axcpr77qOk6+U0nzEn+MGsU6oPSbbxISEuI6joiISJ4zxnD9xx8zBjCvvAI+PqjtyVTSvEBaWhq95s3jpksu4Za77nIdR0REJN+0aNGCSe3akZKaSsp997mOk69U0jzd9u38/PTTrFu3jhdefZXAwEDXiURERPLVI++8w0vAruXL4dAh13HyjUqah0t77DHavf02nRo2pGvXrq7jiIiI5Lv69euz/ZZbqLl/P1sPH3YdJ9+opHmyOXMI+vpr3gSeePttMs+OJSIi4n/+89JLpGZk8M7AgTBxous4+UIlzVOlpZF+331sCQhgYbt2tGnTxnUiERERZ6pVq8a9997LxSNHknHDDbB2retIeU4lzVO98AKBq1fzUEYG/3n9dddpREREnHvuued4PTSUI8bATTfBsWOuI+UplTQPdaB0aYYGBRHUsyeNGzd2HUdERMS5cuXKccujj3JTSgqsWAEPPug6Up5SSfM01mKt5Y5Zs3g4MJCYmBjXiURERDzGE088wfLy5fmkXDn45BMYPdp1pDyjcwt5kvR0uP56FkZGMn78eF577TVq1KjhOpWIiIjHKFasGEOGDKFn9+7UatmSFm3buo6UZ1TSPMlLL8GkScQWKULjxo157LHHXCcSERHxON26daN7z560nTCBZXv2ULtkSUhNhYIFXUfLVcZa6zpDrouKirKLFy92HePc/PorXHUVc6pWpW1CAosXL+bSSy91nUpERMQj7dy5k9q1a1OrRg3mhIRgIiPhiy/AC4erMsYssdZGnTxd+6R5gm3boHdvjlSqRKdNm3jmmWdU0ERERM6gXLlyvPfee/y2YAHzCxaEkSPh/fddx8pVKmmeYOJE7OHDdEtNJaJ2bQYOHOg6kYiIiMfr3bs3nTt3psPs2Rxt3x4eegiGDXMdK9eopHmCfv145vrr+XX3bj777DNCQkJcJxIREfF4xhiGDh2KCQqiR2oqtnNnuPdeGDXKdbRcoZLmyr590K4dLF7MlClTeD02locffpimTZu6TiYiIuI1qlSpwptvvslPM2fyQevW0KcPNGniOlauUElz4cAB6NAB5s5lw4IF3HjjjTRs2JCXXnrJdTIRERGvc88993Ddddfx8NNP80OPHlCzJlgLy5a5jnZBVNLy26FD0KkTrFjBrqFDaRkTQ8mSJZk0aRJhYWGu04mIiHidgIAAvvrqKxo1akSvXr1YvHgxDBmSuUZtxAjX8c6bSlp+2roVoqJg8WKOfvYZ7d55h6NHjzJlyhQqVqzoOp2IiIjXKlSoED/88ANlypThmmuuIb51a2jZEvr2hXvu8crzfKqk5afy5aFZM1J//JGuX3zB2rVrGTt2LHXr1nWdTERExOuVL1+eqVOnkpycTKeePdn/zTfw7LOZp49q2hTWrnUd8ZyopOW1ffvgzjszx0ILDMSOGME9X37J9OnT+eSTT2jXrp3rhCIiIj6jVq1ajB8/ng0bNtCtZ0+SBw2CqVNhz57M72QvopKWV6yFKVOgQYPMQ4F//50jR45wxx13MGLECF544QX69OnjOqWIiIjPadWqFZ9//jmzZs3immuuYWfDhrBxIzRvnjnDBx/A+vVuQ+aASlpe+OGHzJ0Vu3SBkBCYN4/lF11EVFQUI0eOZNCgQQwaNMh1ShEREZ/Vu3dvPvnkE+bOncull17KL3PnZt6xZw888wzUqgV33AGbN7sNegYqaecoNjaWyMhIAgICiIyMJDY2NvOOjIz/n2nSpMxhNj79FLtqFR/Mn0+zZs04fPgw06dP58UXX8R44bnFREREvMldd93FokWLKFWqFB06dODpp58mtVgx2LAh8+wEX38NF1+cOQDuzp0nHnfa7/r8Zq31uUvjxo1tXhg1apQNCwuzgAVsebB3FyhgNzVvbm3Zstb+9lvmjAcPWpuaauPj423Xrl0tYDt37mx37dqVJ7lERETk9I4ePWrvueceC9hmzZrZlStX2oyMDGu3brX2/vutLVHC2p07rbXW/jRokG0eGnriux6wYWFhdtSoUXmWD1hsT9FnTOZ9viUqKsouXrw41583MjKS+Ph4woFJQL2s6bsDAihz882kR0czPzCQyZMnM3nyZFasWEFwcDCvv/46Dz/8sNaeiYiIOPTtt99yzz33cOjQIS666CK6dOlCly5daNW4MSGlSnHs2DH+KFaMZqmpJAK9gN+yHhsREUFcXFye5DLGLLHWRv1rukpazgUEBGCtJQgYC8wBfgZWAJWrVOHo0aPs27ePwMBArrjiCrp06UK3bt2oXr16rmcRERGRc7d9+3bGjRvH5MmT+fXXXzl+/DiFChWiXLlybN68mVLW0gXoCvQHdmQ9zhhDRvZdm3KRSlou+HtN2smKFy9Oly5dCA4OplOnTnTo0IHixYvn+uuLiIhI7klKSmLGjBlMnjyZvXv3Urt2bT744AP27Nnzr3ldrEkLypNX81ExMTFER0eTlJR0YlpYWBgffPABvXv3dphMREREzlVYWNiJTZ5/q169+im/62NiYvI9n47uPAe9e/dm2LBhREREYIwhIiKCYcOGqaCJiIj4CE/6rtfmThERERGHTre5U2vSRERERDyQk5JmjOlpjFltjMkwxvyrOWab72pjzDpjzAZjzNP5mVFERETEJVdr0lYB3YHZp5vBGBMIfAh0AmoDNxtjaudPPBERERG3nBzdaa1dA5xtcNfLgA3W2k1Z844mc9iSP/M8oIiIiIhjnrxPWiVgS7bbiVnTTskYE22MWWyMWbx79+48DyciIiKSl/JsTZoxZhpQ/hR3DbTWTsjJU5xi2mkPRbXWDgOGQebRnTkKKSIiIuKh8qykWWuvusCnSASqZLtdGdh2gc8pIiIi4hU8eXPnIqCGMaaqMaYAmec5neg4k4iIiEi+cDUERzdjTCJwOTDZGPNT1vSKxpgpANbaNGAA8BOwBvjWWrvaRV4RERGR/Obq6M5xwLhTTN8GdM52ewowJR+jiYiIiHgET97cKSIiIuK3fPLcncaY3UB8Hr9MaWBPHr+Gp/LnZQf/Xn5/Xnbw7+XXsvsvf17+/Fr2CGttmZMn+mRJyw/GmMWnOhmqP/DnZQf/Xn5/Xnbw7+XXsvvnsoN/L7/rZdfmThEREREPpJImIiIi4oFU0s7fMNcBHPLnZQf/Xn5/Xnbw7+XXsvsvf15+p8uufdJEREREPJDWpImIiIh4IJW0MzDG9DTGrDbGZBhjTnt0hzHmamPMOmPMBmPM09mmlzTG/GKM+SvrZ4n8SX7hcpLdGFPTGLM82+WQMebhrPteMMZszXZf53+9iIfK6ftmjIkzxqzMWr7F5/p4T5XD976KMWaGMWZN1mfkoWz3ed17f7rPcLb7jTFmcNb9K4wxjXL6WE+Xg2XvnbXMK4wx84wxl2a775SfAW+Sg+VvbYw5mO33eVBOH+vpcrDsT2Rb7lXGmHRjTMms+7z6vTfGfGaM2WWMWXWa+z3jM2+t1eU0F6AWUBOYCUSdZp5AYCNQDSgA/AHUzrrvDeDprOtPA6+7XqZzWPZzyp7177CDzLFeAF4AHne9HHm57EAcUPpC/+087ZKT/EAFoFHW9SLA+my/91713p/pM5xtns7AVMAAzYAFOX2sJ19yuOzNgRJZ1zv9vexZt0/5GfCWSw6XvzUw6Xwe68mXc80PXAv86kPvfUugEbDqNPd7xGdea9LOwFq7xlq77iyzXQZssNZustamAKOBrln3dQVGZF0fAVyfJ0HzxrlmbwdstNbm9SDC+eFC3zdvft8hB/mttduttUuzrh8m8/y6lfIrYC4702f4b12BkTbTfKC4MaZCDh/ryc6a31o7z1q7P+vmfKByPmfMSxfy/vn8e3+Sm4Gv8yVZPrDWzgb2nWEWj/jMq6RduErAlmy3E/n/L6ty1trtkPmlBpTN52wX4lyz9+LfH+ABWauJP/OyTX45XXYL/GyMWWKMiT6Px3uqc8pvjIkEGgILsk32pvf+TJ/hs82Tk8d6snPNfxeZaxf+drrPgLfI6fJfboz5wxgz1RhT5xwf66lynN8YEwZcDYzJNtnb3/uz8YjPvJMTrHsSY8w0oPwp7hporZ2Qk6c4xTSvOGT2TMt+js9TALgOeCbb5I+Al8j8t3gJeBu48/yS5r5cWvYW1tptxpiywC/GmLVZ/zvzeLn43hcm8w/3w9baQ1mTPfq9P4WcfIZPN4/Xfv6z5Di/MaYNmSXtimyTvfYzkCUny7+UzN04jmTtXzkeqJHDx3qyc8l/LfCbtTb7midvf+/PxiM+835f0qy1V13gUyQCVbLdrgxsy7q+0xhTwVq7PWs16a4LfK1cdaZlN8acS/ZOwFJr7c5sz33iujFmODApNzLnltxYdmvttqyfu4wx48hcDT4bD3/fIXeW3xgTTGZBi7XWjs323B793p/CmT7DZ5unQA4e68lysuwYY+oDnwCdrLV7/55+hs+Atzjr8mf7zwfW2inGmCHGmNI5eayHO5f8/9pS4gPv/dl4xGdemzsv3CKghjGmatYapV7AxKz7JgJ9sq73AXKyZs5TnEv2f+2rkPXl/rduwCmPoPFQZ112Y0whY0yRv68DHfj/ZfTm9x1ytvwG+BRYY61956T7vO29P9Nn+G8TgduzjvhqBhzM2hSck8d6srPmN8aEA2OB26y167NNP9NnwFvkZPnLZ/2+Y4y5jMzvzb05eayHy1F+Y0wxoBXZ/g74yHt/Np7xmc+rIxJ84ULmF0wikAzsBH7Kml4RmJJtvs5kHt22kczNpH9PLwVMB/7K+lnS9TKdw7KfMvsplj2MzD9YxU56/JfASmBF1i9wBdfLlJvLTuaRPX9kXVb7yvt+Dst/BZmr+FcAy7Munb31vT/VZxjoB/TLum6AD7PuX0m2o71P9/n3lksOlv0TYH+293lx1vTTfga86ZKD5R+QtXx/kHngRHN/ee+zbvcFRp/0OK9/78lcsbAdSCXze/4uT/zM64wDIiIiIh5ImztFREREPJBKmoiIiIgHUkkTERER8UAqaSIiIiIeSCVNRERExAOppImIiIh4IJU0EREREQ+kkiYicgbGmCZZJ4sPzRppfbUxpq7rXCLi+zSYrYjIWRhjXgZCgYJAorX2VceRRMQPqKSJiJxF1jn6FgHHyTwtULrjSCLiB7S5U0Tk7EoChYEiZK5RExHJc1qTJiJyFsaYicBooCqZJ4wf4DiSiPiBINcBREQ8mTHmdiDNWvuVMSYQmGeMaWut/dV1NhHxbVqTJiIiIuKBtE+aiIiIiAdSSRMRERHxQCppIiIiIh5IJU1ERETEA6mkiYiIiHgglTQRERERD6SSJiIiIuKBVNJEREREPND/AdIlQvFy7zSUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_best_state(train_state)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68abe04f",
   "metadata": {},
   "source": [
    "我们看到在边界上(x=1或-1附近)拟合的不是特别好，但是如果采样点取多一些，情况会变好。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "463eeb9c",
   "metadata": {},
   "source": [
    "## 学到更多\n",
    "\n",
    "https://deepxde.readthedocs.io/en/latest/demos/function.html\n",
    "\n",
    "### 函数逼近演示\n",
    "\n",
    "下面是一些学习功能的演示。\n",
    "\n",
    "#### 函数逼近\n",
    "\n",
    "- [从数据集中学习函数](https://github.com/lululxvi/deepxde/blob/master/examples/function/dataset.py)\n",
    "- [从公式中学习一个函数](https://github.com/lululxvi/deepxde/blob/master/examples/function/func.py)\n",
    "\n",
    "#### 不确定性量化\n",
    "\n",
    "- [学习具有不确定性量化的函数](https://github.com/lululxvi/deepxde/blob/master/examples/function/func_uncertainty.py)\n",
    "\n",
    "#### 多保真学习\n",
    "\n",
    "- [从公式中进行多保真学习](https://github.com/lululxvi/deepxde/blob/master/examples/function/mf_func.py)\n",
    "- [从数据集中进行多保真学习](https://github.com/lululxvi/deepxde/blob/master/examples/function/mf_dataset.py)"
   ]
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   "source": []
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